Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization

Bioengineering, Journal Year: 2022, Volume and Issue: 10(1), P. 18 - 18

Published: Dec. 22, 2022

Diagnosing a brain tumor takes long time and relies heavily on the radiologist's abilities experience. The amount of data that must be handled has increased dramatically as number patients increased, making old procedures both costly ineffective. Many researchers investigated variety algorithms for detecting classifying tumors were accurate fast. Deep Learning (DL) approaches have recently been popular in developing automated systems capable accurately diagnosing or segmenting less time. DL enables pre-trained Convolutional Neural Network (CNN) model medical images, specifically cancers. proposed Brain Tumor Classification Model based CNN (BCM-CNN) is hyperparameters optimization using an adaptive dynamic sine-cosine fitness grey wolf optimizer (ADSCFGWO) algorithm. There followed by training built with Inception-ResnetV2. employs commonly used models (Inception-ResnetV2) to improve diagnosis, its output binary 0 1 (0: Normal, 1: Tumor). are primarily two types hyperparameters: (i) determine underlying network structure; (ii) hyperparameter responsible network. ADSCFGWO algorithm draws from sine cosine adaptable framework uses algorithms' strengths. experimental results show BCM-CNN classifier achieved best due enhancement CNN's performance optimization's hyperparameters. 99.98% accuracy BRaTS 2021 Task dataset.

Language: Английский

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Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm

Mathematics, Journal Year: 2022, Volume and Issue: 10(19), P. 3614 - 3614

Published: Oct. 2, 2022

The world is still trying to recover from the devastation caused by wide spread of COVID-19, and now monkeypox virus threatens becoming a worldwide pandemic. Although not as lethal or infectious numerous countries report new cases daily. Thus, it surprising that necessary precautions have been taken, will be if another pandemic occurs. Machine learning has recently shown tremendous promise in image-based diagnosis, including cancer detection, tumor cell identification, COVID-19 patient detection. Therefore, similar application may implemented diagnose invades human skin. An image can acquired utilized further condition. In this paper, two algorithms are proposed for improving classification accuracy images. based on transfer feature extraction meta-heuristic optimization selection parameters multi-layer neural network. GoogleNet deep network adopted extraction, Al-Biruni Earth radius algorithm, sine cosine particle swarm algorithm. Based these algorithms, binary hybrid algorithm selection, along with optimizing To evaluate publicly available dataset employed. assessment was performed terms ten evaluation criteria. addition, set statistical tests conducted measure effectiveness, significance, robustness algorithms. results achieved confirm superiority effectiveness methods compared other methods. average 98.8%.

Language: Английский

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Utilizing convolutional neural networks to classify monkeypox skin lesions

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 3, 2023

Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the be challenging time-consuming, especially resource-limited settings where laboratory tests may not available. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown great potential image recognition classification tasks. To this end, study proposes an approach using CNNs to classify lesions. Additionally, optimized CNN model Grey Wolf Optimizer (GWO) algorithm, resulting significant improvement accuracy, precision, recall, F1-score, AUC compared non-optimized model. The GWO optimization strategy enhance performance models similar achieved impressive accuracy 95.3%, indicating optimizer has improved model's ability discriminate between positive negative classes. proposed several benefits for improving efficiency diagnosis surveillance. It could enable faster more accurate lesions, leading earlier detection better patient outcomes. Furthermore, crucial public health implications controlling preventing outbreaks. Overall, offers novel highly effective monkeypox, which real-world applications.

Language: Английский

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Waterwheel Plant Algorithm: A Novel Metaheuristic Optimization Method

Processes, Journal Year: 2023, Volume and Issue: 11(5), P. 1502 - 1502

Published: May 15, 2023

Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), novel stochastic motivated by natural systems. The proposed WWPA’s basic concept based on modeling plant’s behavior while hunting expedition. To find prey, WWPA uses plants as search agents. We present mathematical model for use addressing problems. Twenty-three objective functions of varying unimodal multimodal types were used assess performance. results optimizing demonstrate strong exploitation ability get close optimal solution, show exploration zero major region space. Three engineering design also gauge potential improving practical programs. effectiveness was evaluated comparing its with those seven widely metaheuristic algorithms. When compared eight competing algorithms, simulation analyses that outperformed them finding more proportionate balance between exploitation.

Language: Английский

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An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

Diagnostics, Journal Year: 2022, Volume and Issue: 12(11), P. 2892 - 2892

Published: Nov. 21, 2022

Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals developed countries experiencing monkeypox. Such conditions often carry less obvious but no devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due the visual resolution monkeypox disease images, medical specialists high-level tools are typically required for a proper diagnosis. The manual diagnosis is subjective, time-consuming, labor-intensive. Therefore, it necessary create computer-aided approach automated disease. Most research articles on relied convolutional neural networks (CNNs) using classical loss functions, allowing them pick up discriminative elements images. To enhance this, novel framework Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) proposed fine-tune deep CNN layers classifying from As first step approach, we use CNN-based models learn embedding input images Euclidean space. In second step, an optimized classification model based triplet function calculate distance between pairs space features that may be used distinguish different cases, cases. uses human obtained African hospital. experimental results study demonstrate framework’s efficacy, as outperforms numerous examples prior problems. On other hand, statistical experiments Wilcoxon analysis variance (ANOVA) tests conducted evaluate terms effectiveness stability. recorded confirm superiority method when compared optimization algorithms machine learning models.

Language: Английский

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Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 23681 - 23700

Published: Jan. 1, 2023

Oral cancer is a deadly form of cancerous tumor that widely spread in low and middle-income countries. An early affordable oral diagnosis might be achieved by automating the detection precancerous malignant lesions mouth. There are many research attempts to develop robust machine-learning model can detect from images. However, these still lacking high precision detection. Therefore, this work aims propose new approach capable detecting medical images with higher accuracy. In work, novel based on convolutional neural network (CNN) optimized deep belief (DBN). The design parameters CNN DBN using optimization algorithm, which developed as hybrid Particle Swarm Optimization (PSO) Al-Biruni Earth Radius (BER) algorithms denoted (PSOBER). Using standard biomedical dataset available Kaggle repository, proposed shows promising results outperforming various competing approaches an accuracy 97.35%. addition, set statistical tests, such One-way analysis-of-variance (ANOVA) Wilcoxon signed-rank conducted prove significance stability approach. methodology solid efficient, specialists adopt it. additional larger scale required confirm findings highlight other features utilized for

Language: Английский

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Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 79750 - 79776

Published: Jan. 1, 2023

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase efficacy classification algorithms, it necessary to identify relevant subset features in a given domain. This means that challenge can be seen as an optimization problem, thus meta-heuristic techniques utilized find solution. Methodology: this work, we propose novel hybrid binary algorithm solve problem by combining two algorithms: Dipper Throated Optimization (DTO) Sine Cosine (SC) algorithm. The new referred bSCWDTO. We employed sine cosine improve exploration process ensure converges quickly accurately. Thirty datasets from University California Irvine (UCI) machine learning repository are used evaluate robustness stability proposed bSCWDTO addition, K-Nearest Neighbor (KNN) classifier measure selected features' effectiveness problems. Results: achieved results demonstrate algorithm's superiority over ten state-of-the-art methods, including original DTO SC, Particle Swarm (PSO), Whale Algorithm (WOA), Grey Wolf (GWO), Multiverse (MVO), Satin Bowerbird Optimizer (SBO), Genetic (GA), GWO GA, Firefly (FA). Moreover, Wilcoxon's rank-sum test was performed at 0.05 significance level study statistical difference between method alternative methods. Conclusion: These emphasized method's significance, superiority, difference.

Language: Английский

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A Binary Waterwheel Plant Optimization Algorithm for Feature Selection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 94227 - 94251

Published: Jan. 1, 2023

The vast majority of today's data is collected and stored in enormous databases with a wide range characteristics that have little to do the overarching goal concept. Feature selection process choosing best features for classification problem, which improves classification's accuracy. considered multi-objective optimization problem two objectives: boosting accuracy while decreasing feature count. To efficiently handle process, we propose this paper novel algorithm inspired by behavior waterwheel plants when hunting their prey how they update locations throughout exploration exploitation processes. proposed referred as binary plant (bWWPA). In particular approach, search space well technique's mapping from continuous discrete spaces are both represented new model. Specifically, fitness cost functions factored into algorithm's evaluation modeled mathematically. assess performance algorithm, set extensive experiments were conducted evaluated terms 30 benchmark datasets include low, medium, high dimensional features. comparison other recent algorithms, experimental findings demonstrate bWWPAperforms better than competing algorithms. addition, statistical analysis performed one-way analysis-of-variance (ANOVA) Wilcoxon signed-rank tests examine differences between compared These experiments' results confirmed superiority effectiveness handling process.

Language: Английский

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BAOA: Binary Arithmetic Optimization Algorithm With K-Nearest Neighbor Classifier for Feature Selection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 94094 - 94115

Published: Jan. 1, 2023

The Arithmetic Optimization Algorithm (AOA) is a recently proposed metaheuristic algorithm that has been shown to perform well in several benchmark tests. AOA uses the main arithmetic operators' distribution behavior, such as multiplication, division, subtraction, and addition. This paper proposes binary version of (BAOA) tackle feature selection problem classification. algorithm's search space converted from continuous one using sigmoid transfer function meet nature task. classifier method known wrapper-based approach K-Nearest Neighbors (KNN), find best possible solutions. study 18 datasets University California, Irvine (UCI) repository evaluate suggested performance. results demonstrate BAOA outperformed Binary Dragonfly (BDF), Particle Swarm (BPSO), Genetic (BGA), Cat (BCAT) when various performance metrics were used, including classification accuracy, selected features worst optimum fitness values.

Language: Английский

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A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(1), P. e0295579 - e0295579

Published: Jan. 2, 2024

This paper proposes a feature selection method based on hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, noisy features within high-dimensional datasets. Drawing inspiration from Chinese idiom “Chai Lang Hu Bao,” mechanisms, cooperative behaviors observed in natural animal populations, we amalgamate GWO algorithm, Lagrange interpolation method, GJO propose multi-strategy fusion GJO-GWO algorithm. In Case 1, addressed eight complex benchmark functions. 2, was utilized tackle ten problems. Experimental results consistently demonstrate under identical experimental conditions, whether solving functions or addressing problems, exhibits smaller means, lower standard deviations, higher classification accuracy, reduced execution times. These findings affirm superior performance, stability

Language: Английский

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